scholarly journals An Interpolation-Based Polynomial Method of Estimating the Objective Function Value in Scheduling Problems of Minimizing the Maximum Lateness

2021 ◽  
Author(s):  
Alexander Alekseevich Lazarev ◽  
Darya Vladimirovna Lemtyuzhnikova ◽  
Andrey Alexandrovich Tyunyatkin
Keyword(s):  
Objective Function ◽  
Objective Function Value ◽  
Maximum Lateness ◽  
Polynomial Method ◽  
Scheduling Problems ◽  
Polynomially Solvable ◽  
Interpolation Nodes

An approach to estimating the objective function value of minimization maximum lateness problem is proposed. It is shown how to use transformed instances to define a new continuous objective function. After that, using this new objective function, the approach itself is formulated. We calculate the objective function value for some polynomially solvable transformed instances and use them as interpolation nodes to estimate the objective function of the initial instance. What is more, two new polynomial cases, that are easy to use in the approach, are proposed. In the end of the paper numeric experiments are described and their results are provided.

scholarly journals Chip Attach Scheduling in Semiconductor Assembly

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Zhicong Zhang ◽  
Kaishun Hu ◽  
Shuai Li ◽  
Huiyu Huang ◽  
Shaoyong Zhao
Keyword(s):  
Objective Function ◽  
Objective Function Value ◽  
Learning Algorithm ◽  
Parallel Machine Scheduling ◽  
Volume Index ◽  
Production Volume ◽  
Learning Problem ◽  
Reward Function ◽  
Unrelated Parallel Machine Scheduling ◽  
Target Production

Chip attach is the bottleneck operation in semiconductor assembly. Chip attach scheduling is in nature unrelated parallel machine scheduling considering practical issues, for example, machine-job qualification, sequence-dependant setup times, initial machine status, and engineering time. The major scheduling objective is to minimize the total weighted unsatisfied Target Production Volume in the schedule horizon. To apply Q-learning algorithm, the scheduling problem is converted into reinforcement learning problem by constructing elaborate system state representation, actions, and reward function. We select five heuristics as actions and prove the equivalence of reward function and the scheduling objective function. We also conduct experiments with industrial datasets to compare the Q-learning algorithm, five action heuristics, and Largest Weight First (LWF) heuristics used in industry. Experiment results show that Q-learning is remarkably superior to the six heuristics. Compared with LWF, Q-learning reduces three performance measures, objective function value, unsatisfied Target Production Volume index, and unsatisfied job type index, by considerable amounts of 80.92%, 52.20%, and 31.81%, respectively.

scholarly journals Joint admission control and power allocation in hospital networks based on cognitive radios.

2021 ◽  
Author(s):  
Yicong Liu
Keyword(s):  
Objective Function ◽  
Admission Control ◽  
Objective Function Value ◽  
Cognitive Radios ◽  
Hospital Environment ◽  
Mixed Integer ◽  
Total System ◽  
Secondary Users ◽  
Source Power ◽  
Minlp Problem

In this thesis, we present an approach to solve the joint call admission control and power allo- cation problem in a hospital environment based on cognitive radio. Specifically, a multi-objective non-convex mixed integer non-linear programming (MINLP) problem with weighted-sum method for wireless access in an indoor hospital environment has been formulated in order to maximize the number of admitted secondary users and minimize transmit power while guaranteeing the through- put of all secondary users and satisfying the interference constraints for the protected and primary users. To solve this MINLP problem with different weights given to different objectives, we pro- pose to use the standard branch and bound algorithm as appropriately modified to find the optimal solution. We also coded a specific program using OPTI Toolbox to find the minimum objective function value, number of admitted secondary users and all related values such as total system power and throughput. To analyze the numerical results, we considered three cases with equal and non-equal weights. We also changed the values of interference and maximum source power to obtain and analyze different results comparing with the normal one. Our results indicate that more power is allocated and better throughput is guaranteed while the number of admitted users is increasing. However, as they increase, the objective function value increases steadily as well, which means that it is more difficult to reach our minimizing objective.

Modified Harmony Search Algorithm for 0-1 Knapsack Problems

2013 ◽  
Vol 365-366 ◽  
pp. 182-185
Author(s):  
Hong Gang Xia ◽  
Qing Liang Wang
Keyword(s):  
Convergence Rate ◽  
Objective Function ◽  
Objective Function Value ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Knapsack Problems ◽  
Convergence And Stability ◽  
Update Strategy ◽  
Harmony Memory

In this paper, a modified harmony search (MHS) algorithm was presented for solving 0-1 knapsack problems. MHS employs position update strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. Besides, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory, and the key parameters PAR and BW dynamically adjusted with the number of generation. Based on the experiment of solving ten classic 0-1 knapsack problems, the MHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS).

scholarly journals Weak and Strong Superiorization: Between Feasibility-Seeking and Minimization

2015 ◽  
Vol 23 (3) ◽  
pp. 41-54 ◽  
Author(s):  
Yair Censor
Keyword(s):  
Objective Function ◽  
Minimization Problem ◽  
General Principle ◽  
Objective Function Value ◽  
Constrained Minimization ◽  
Feasible Point ◽  
Research Directions ◽  
Constrained Minimization Problem

Abstract We review the superiorization methodology, which can be thought of, in some cases, as lying between feasibility-seeking and constrained minimization. It is not quite trying to solve the full edged constrained minimization problem; rather, the task is to find a feasible point which is superior (with respect to an objective function value) to one returned by a feasibility-seeking only algorithm. We distinguish between two research directions in the superiorization methodology that nourish from the same general principle: Weak superiorization and strong superiorization and clarify their nature.

An Optimal Missile Autopilot Design Model

2018 ◽  
Vol 14 (1) ◽  
pp. 104-110
Author(s):  
Yong-chao Chen ◽  
Xin-bao Gao ◽  
Min Gao ◽  
Dan Fang
Keyword(s):  
Optimal Design ◽  
Objective Function ◽  
Design Process ◽  
Objective Function Value ◽  
Design Method ◽  
Response Speed ◽  
Performance Indexes ◽  
Optimal Design Method ◽  
Objective Function Values ◽  
Constrained Conditions

This article describes how one optimal design method is given to the design of missile autopilots. This method profits from an exhaustive method. By this method, the design process of a missile autopilot is simplified, and the design efficiency is improved. In the design process of this method, the performance indexes of autopilot are translated into constraint conditions, and the response speed is translated to an objective function. Thus, the optimal design of missile autopilot is translated into the optimal design of a nonlinear system with multiple constraints. The optimization algorithm is found to be out of controller parameter combinations which can satisfy constrained conditions. Firstly, calculations of the corresponding objective function values. Second, by the extract the optimal combination which has the minimal objective function value.

scholarly journals Quantum State Optimization and Computational Pathway Evaluation for Gate-Model Quantum Computers

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Laszlo Gyongyosi
Keyword(s):  
Objective Function ◽  
Quantum State ◽  
Quantum Computer ◽  
Objective Function Value ◽  
Evaluation Procedure ◽  
Target System ◽  
Quantum Computers ◽  
System State ◽  
Computational Problem ◽  
State Determination

AbstractA computational problem fed into a gate-model quantum computer identifies an objective function with a particular computational pathway (objective function connectivity). The solution of the computational problem involves identifying a target objective function value that is the subject to be reached. A bottleneck in a gate-model quantum computer is the requirement of several rounds of quantum state preparations, high-cost run sequences, and multiple rounds of measurements to determine a target (optimal) state of the quantum computer that achieves the target objective function value. Here, we define a method for optimal quantum state determination and computational path evaluation for gate-model quantum computers. We prove a state determination method that finds a target system state for a quantum computer at a given target objective function value. The computational pathway evaluation procedure sets the connectivity of the objective function in the target system state on a fixed hardware architecture of the quantum computer. The proposed solution evolves the target system state without requiring the preparation of intermediate states between the initial and target states of the quantum computer. Our method avoids high-cost system state preparations and expensive running procedures and measurement apparatuses in gate-model quantum computers. The results are convenient for gate-model quantum computations and the near-term quantum devices of the quantum Internet.

Optimization Design Method of Sealing Concrete and Ballast

2011 ◽  
Vol 368-373 ◽  
pp. 390-394
Author(s):  
Mei Liang Yang ◽  
Zhen Hai Zeng ◽  
Fang Ping Zhong ◽  
Zhen Hua Li
Keyword(s):  
Objective Function ◽  
Optimization Design ◽  
Objective Function Value ◽  
Design Method ◽  
Optimal Solution ◽  
Xiangjiang River ◽  
Economical Efficiency ◽  
Independent Variables ◽  
Unfavorable Condition ◽  
Two Independent Variables

As defined in optimization ideas, economical efficiency is the objective function, while the volume of sealing concrete and ballast, two independent variables, exerts impacts on the objective function value through economic parameters respectively. Constraints are founded according to the strength and stability requirements of sealing concrete and steel box, in the most unfavorable condition. At the last, Matlab is used to achieve optimal solution. This paper combined Xiangjiang River Grand Bridge Project in Changxiang express way and achieved good practical results.

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